


import argparse
import os
import re

import packaging.version


__UpperCamelCase     		=							'examples/'
__UpperCamelCase     		=							{
    'examples': (re.compile(R'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(R'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(R'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), R'\1version="VERSION",'),
    'doc': (re.compile(R'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
__UpperCamelCase     		=							{
    'init': 'src/diffusers/__init__.py',
    'setup': 'setup.py',
}
__UpperCamelCase     		=							'README.md'
def 							UpperCamelCase_(					_A :List[Any]					,  _A :Union[str, Any]					,  _A :Optional[Any]	)->		str:
			with open(_A					,  "r"					,  encoding="utf-8"					,  newline="\n"	) as f:
						UpperCamelCase__       =							f.read()
			UpperCamelCase__,   UpperCamelCase__       =							REPLACE_PATTERNS[pattern]
			UpperCamelCase__       =							replace.replace("VERSION"					,  _A	)
			UpperCamelCase__       =							re_pattern.sub(_A					,  _A	)
			with open(_A					,  "w"					,  encoding="utf-8"					,  newline="\n"	) as f:
						f.write(_A	)
def 							UpperCamelCase_(					_A :int	)->		Union[str, Any]:
			for folder, directories, fnames in os.walk(_A	):
						# Removing some of the folders with non-actively maintained examples from the walk
						if "research_projects" in directories:
									directories.remove("research_projects"	)
						if "legacy" in directories:
									directories.remove("legacy"	)
						for fname in fnames:
									if fname.endswith(".py"	):
												update_version_in_file(os.path.join(_A					,  _A	)					,  _A					,  pattern="examples"	)
def 							UpperCamelCase_(					_A :str					,  _A :Optional[Any]=False	)->		Optional[int]:
			for pattern, fname in REPLACE_FILES.items():
						update_version_in_file(_A					,  _A					,  _A	)
			if not patch:
						update_version_in_examples(_A	)
def 							UpperCamelCase_(					)->		Tuple:
			UpperCamelCase__       =							"🤗 Transformers currently provides the following architectures"
			UpperCamelCase__       =							"1. Want to contribute a new model?"
			with open(_A					,  "r"					,  encoding="utf-8"					,  newline="\n"	) as f:
						UpperCamelCase__       =							f.readlines()

			# Find the start of the list.
			UpperCamelCase__       =							0
			while not lines[start_index].startswith(_start_prompt	):
						start_index += 1
			start_index += 1

			UpperCamelCase__       =							start_index
			# Update the lines in the model list.
			while not lines[index].startswith(_end_prompt	):
						if lines[index].startswith("1."	):
									UpperCamelCase__       =							lines[index].replace(
									    "https://huggingface.co/docs/diffusers/main/model_doc"					,  "https://huggingface.co/docs/diffusers/model_doc"					,  )
						index += 1

			with open(_A					,  "w"					,  encoding="utf-8"					,  newline="\n"	) as f:
						f.writelines(_A	)
def 							UpperCamelCase_(					)->		Dict:
			with open(REPLACE_FILES["init"]					,  "r"	) as f:
						UpperCamelCase__       =							f.read()
			UpperCamelCase__       =							REPLACE_PATTERNS["init"][0].search(_A	).groups()[0]
			return packaging.version.parse(_A	)
def 							UpperCamelCase_(					_A :Optional[Any]=False	)->		int:
			UpperCamelCase__       =							get_version()
			if patch and default_version.is_devrelease:
						raise ValueError("Can't create a patch version from the dev branch, checkout a released version!"	)
			if default_version.is_devrelease:
						UpperCamelCase__       =							default_version.base_version
			elif patch:
						UpperCamelCase__       =							F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
			else:
						UpperCamelCase__       =							F'''{default_version.major}.{default_version.minor + 1}.0'''

			# Now let's ask nicely if that's the right one.
			UpperCamelCase__       =							input(F'''Which version are you releasing? [{default_version}]'''	)
			if len(_A	) == 0:
						UpperCamelCase__       =							default_version

			print(F'''Updating version to {version}.'''	)
			global_version_update(_A					,  patch=_A	)
def 							UpperCamelCase_(					)->		Tuple:
			UpperCamelCase__       =							get_version()
			UpperCamelCase__       =							F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
			UpperCamelCase__       =							current_version.base_version

			# Check with the user we got that right.
			UpperCamelCase__       =							input(F'''Which version are we developing now? [{dev_version}]'''	)
			if len(_A	) == 0:
						UpperCamelCase__       =							dev_version

			print(F'''Updating version to {version}.'''	)
			global_version_update(_A	)


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
						__UpperCamelCase     		=							argparse.ArgumentParser()
						parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
						parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
						__UpperCamelCase     		=							parser.parse_args()
						if not args.post_release:
												pre_release_work(patch=args.patch)
						elif args.patch:
												print('Nothing to do after a patch :-)')
						else:
												post_release_work()






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       int 			=		(DDIMParallelScheduler,)
			_UpperCamelCase		:       List[Any] 			=		(('eta', 0.0), ('num_inference_steps', 50))




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							{
						    "num_train_timesteps": 1000,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "clip_sample": True,
						}

						config.update(**snake_case  )
						return config




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config(**snake_case  )
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						UpperCamelCase__,   UpperCamelCase__       =							10, 0.0

						UpperCamelCase__       =							self.dummy_model()
						UpperCamelCase__       =							self.dummy_sample_deter

						scheduler.set_timesteps(snake_case  )

						for t in scheduler.timesteps:
									UpperCamelCase__       =							model(snake_case   ,    snake_case  )
									UpperCamelCase__       =							scheduler.step(snake_case   ,    snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						return sample




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for timesteps in [100, 500, 1000]:
									self.check_over_configs(num_train_timesteps=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for steps_offset in [0, 1]:
									self.check_over_configs(steps_offset=snake_case  )

						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config(steps_offset=1  )
						UpperCamelCase__       =							scheduler_class(**snake_case  )
						scheduler.set_timesteps(5  )
						assert torch.equal(scheduler.timesteps   ,    torch.LongTensor([801, 601, 401, 201, 1]  )  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,    [0.002, 0.02, 0.2, 2]  ):
									self.check_over_configs(beta_start=snake_case   ,    beta_end=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for timestep_spacing in ["trailing", "leading"]:
									self.check_over_configs(timestep_spacing=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for rescale_betas_zero_snr in [True, False]:
									self.check_over_configs(rescale_betas_zero_snr=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self.check_over_configs(thresholding=snake_case  )
						for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(
												    thresholding=snake_case   ,    prediction_type=snake_case   ,    sample_max_value=snake_case   ,    )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for t in [1, 10, 49]:
									self.check_over_forward(time_step=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for t, num_inference_steps in zip([1, 10, 50]   ,    [10, 50, 500]  ):
									self.check_over_forward(time_step=snake_case   ,    num_inference_steps=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for t, eta in zip([1, 10, 49]   ,    [0.0, 0.5, 1.0]  ):
									self.check_over_forward(time_step=snake_case   ,    eta=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config()
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						assert torch.sum(torch.abs(scheduler._get_variance(0   ,    0  ) - 0.0  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(420   ,    400  ) - 0.14771  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(980   ,    960  ) - 0.32460  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(0   ,    0  ) - 0.0  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487   ,    486  ) - 0.00979  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999   ,    998  ) - 0.02  )  ) < 1E-5




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config()
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						UpperCamelCase__,   UpperCamelCase__       =							10, 0.0
						scheduler.set_timesteps(snake_case  )

						UpperCamelCase__       =							self.dummy_model()
						UpperCamelCase__       =							self.dummy_sample_deter
						UpperCamelCase__       =							self.dummy_sample_deter + 0.1
						UpperCamelCase__       =							self.dummy_sample_deter - 0.1

						UpperCamelCase__       =							samplea.shape[0]
						UpperCamelCase__       =							torch.stack([samplea, samplea, samplea]   ,    dim=0  )
						UpperCamelCase__       =							torch.arange(snake_case  )[0:3, None].repeat(1   ,    snake_case  )

						UpperCamelCase__       =							model(samples.flatten(0   ,    1  )   ,    timesteps.flatten(0   ,    1  )  )
						UpperCamelCase__       =							scheduler.batch_step_no_noise(snake_case   ,    timesteps.flatten(0   ,    1  )   ,    samples.flatten(0   ,    1  )   ,    snake_case  )

						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 1147.7904  ) < 1E-2
						assert abs(result_mean.item() - 0.4982  ) < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop()

						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 172.0067  ) < 1E-2
						assert abs(result_mean.item() - 0.223967  ) < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop(prediction_type="v_prediction"  )

						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 52.5302  ) < 1E-2
						assert abs(result_mean.item() - 0.0684  ) < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop(set_alpha_to_one=snake_case   ,    beta_start=0.01  )
						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 149.8295  ) < 1E-2
						assert abs(result_mean.item() - 0.1951  ) < 1E-3







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop(set_alpha_to_one=snake_case   ,    beta_start=0.01  )
						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 149.0784  ) < 1E-2
						assert abs(result_mean.item() - 0.1941  ) < 1E-3


